As AI continues to reshape industries, L&D professionals are under increasing pressure to not only incorporate these advanced technologies but to do so in a manner that is both strategic and impactful. When analyzing the applicability of AI-powered enhancements to the CredSpark product, we have to ask ourselves: do these techno-wonders actually make learning better or are they just the latest trend we’re all pressured to embrace? With the need to quickly upskill a diverse workforce and engage learners more effectively, understanding the potential of AI in learning assessments is more critical than ever – for us, and for you. In this installment of our AI for assessments research, we’ll explore what the data says about how this tool is influencing learning experiences, and dive into the how and why.
In our exploration into the impact of AI on learning assessments, we started at the most fundamental place – is AI really adding value to the learning process? How? Why?
So, does AI add value to the learning process?
The simple answer is yes. Overall, our research showed that AI-powered functionalities in learning assessments were found to produce positive impacts in these areas:
Improved learning outcomes
A recent study found that AI-powered functionalities in diverse e-learning based applications enhanced personalized learning by adapting to learners’ cognitive styles and emotional states, using advanced feedback and predictive analytics, all of which contributed to improved learner engagement and performance. A related paper out of the University of Dehradun in India, found that learners that used AI-powered adaptive learning performed better in assessments, compared with the control group. They found that leveraging AI-powered algorithms to customize learning content led to increased interest, personal relevance, interaction, and a deeper understanding of the subject matter, which then translated into higher levels of proficiency and ownership over learning.
Enhanced organizational outcomes
Improved learning outcomes and learner engagement translate into positive organizational impact, particularly for ROI and operational efficiency in learning initiatives (because personalized training is more efficient and effective), better organizational performance (because of the improved learning outcomes and learner motivation), and increased retention and employee engagement (employees that are experiencing professional growth are happy and want to stay).
Deeper, more actionable insights
AI’s ability to pull in data from multiple sources and apply it to algorithms to give feedback and suggest learning actions in real time makes it highly effective for learners, compared to traditional methods of instruction. Assessment data, learning preferences, and performance measurement, coupled with contextual industry information can also help companies to detect skill gaps and learning needs, allowing them to stay ahead of the curve.
The How and Why of Improved Learning and Organizational Outcomes
- Personalization at scale
What it does: Analyzes user data from multiple sources and suggests content / remediation according to emotional states, learning style, proficiency, learning habits, predictive analytics (see below).
Why it works: Ensures content is relevant for the learner, increasing attention, motivation, ownership, self-efficacy, engagement, and reduces cognitive load, which, in turn, helps learners develop skills faster and retain them for longer.
Examples:
A learning assessment that adapts question / content delivery based on individual performance, providing immediate remediation for those that need it, and increased complexity, for those that need it.
Options for content delivery formats (ie. audio, text-based, scenario-based) based on user preferences.
- Real time feedback
What it does: Provides performance / proficiency evaluations and remediation at the moment of occurrence.
Why it works: Redirects erroneous thinking and keeps habits from becoming internalized, building confidence and self-awareness and increasing capability to internalize complex concepts and learner motivation.
Examples:
Learning assessment platform that provides instant correction for inaccurate responses in compliance training, redirecting the learner immediately to complementary remediation (content-, learning style-, emotional state-based) and retesting until the learner has demonstrated understanding.
- Predictive analytics
What it does: Analyzes learner / organization / market past behavior (using multiple data sources) to predict future behavior, and suggests actions or content based on those predictions.
Why it works: Allows learning platforms to predict skill gaps / performance issues on an individual OR organizational level, and target them for training.
Examples:
Past data shows that 90% of sales people that scored under 60% on the knowledge check for a training on Active Listening, were likely to close 40% less sales. This group of learners is then targeted for coaching / increased training to preemptively prevent underperformance in sales targets.
At the end of the day, these 3 pillars – all powered by AI – have been shown to improve individual and group performance because learners are more engaged and are receiving the content and instruction that they need, when and how they need it; desired behavior is being constantly reinforced; and these tools provide an increased capacity to anticipate future problems so they can be solved before they impact the learner (or organization).
Key takeaways:
- AI-powered capabilities have been shown to have an overall positive impact on learning AND organizational outcomes. (Stay tuned for our upcoming installment on what to watch out for when implementing AI-powered learning tools.)
- Learners tend to experience increased engagement, motivation, ownership and awareness of the learning process, as well as increased skill / knowledge retention and shorter training time to achieve key skill milestones.
- Organizations tend to experience increased satisfaction, employee engagement / retention, improved organizational outcomes, and improved ROI on training programs.
- These results are most directly influenced by the scalability that AI offers in the areas of: personalization, real-time feedback, and predictive analytics, which increase learner motivation and relevance, reinforce desired behavior, and help to predict and avoid future problems.
